Senior GenAI Engineer: Agentic AI & Data Pipelines

Lever, Inc.; Jacksonville (FL); Remote; USD 140,000 - 210,000; Full time

Job summary

Dun & Bradstreet is seeking an experienced AI Engineer to design, build, and operationalize AI-driven solutions for its Analytics organization. You will work with data scientists, MLOps engineers, and stakeholders to deploy scalable AI systems using Python, PySpark, and GenAI frameworks.

The role emphasizes agent workflows, GenAI applications, data pipelines, and MLOps in cloud/hybrid environments, with a strong focus on production-grade delivery.

Qualifications

• 7+ years of AI/ML/software experience, with 4+ years in GenAI.
• Proficient in Python, PySpark, and API development.
• Hands-on with LangChain/LangGraph and GenAI frameworks.
• Experience building AI agents, retrieval pipelines, or orchestration.
• Knowledge of prompting, embeddings, RAG, evaluation and tuning.
• Experience with cloud platforms (Azure/AWS/GCP), Docker, and ML CI/CD.
• Strong problem solving and communication skills.

Responsibilities

• Agent Development & Architecture: build agentic workflows and scalable pipelines.
• GenAI Application Engineering: develop GenAI apps with GPT, Gemini, and LLaMA; implement RAG and vector search.
• Data & Platform Engineering: build distributed data pipelines; develop APIs/SDKs; optimize for cloud/hybrid.
• MLOps / LLMOps: contribute to CI/CD, governance, and reusable GenAI frameworks.
• Collaboration & Stakeholder Engagement: work with analytics, product and engineering teams; participate in architecture reviews.

Skills

GenAI engineering

Python

PySpark

API development

Autonomous task orchestration

GenAI concepts

Cloud platforms knowledge

Problem solving

Communication skills

Tools

LangChain

LangGraph

Transformers

OpenAI SDK

Vertex SDK

Bedrock SDK

Docker

Job description

Dun & Bradstreet is seeking an experienced AI Engineer to design, build, and operationalize AI-driven solutions for its Analytics organization. You will work with data scientists, MLOps engineers, and stakeholders to deploy scalable AI systems using Python, PySpark, and GenAI frameworks.

The role emphasizes agent workflows, GenAI applications, data pipelines, and MLOps in cloud/hybrid environments, with a strong focus on production-grade delivery.